Publications

An Exact Framework for Solving the Space-Time Dependent TSP
Isaac Rudich
Quentin Cappart
Manuel L'opez-Ib'anez
Michael Romer
Louis-Martin Rousseau
Many real-world scenarios involve solving bi-level optimization problems in which there is an outer discrete optimization problem, and an in… (voir plus)ner problem involving expensive or black-box computation. This arises in space-time dependent variants of the Traveling Salesman Problem, such as when planning space missions that visit multiple astronomical objects. Planning these missions presents significant challenges due to the constant relative motion of the objects involved. There is an outer combinatorial problem of finding the optimal order to visit the objects and an inner optimization problem that requires finding the optimal departure time and trajectory to travel between each pair of objects. The constant motion of the objects complicates the inner problem, making it computationally expensive. This paper introduces a novel framework utilizing decision diagrams (DDs) and a DD-based branch-and-bound technique, Peel-and-Bound, to achieve exact solutions for such bi-level optimization problems, assuming sufficient inner problem optimizer quality. The framework leverages problem-specific knowledge to expedite search processes and minimize the number of expensive evaluations required. As a case study, we apply this framework to the Asteroid Routing Problem (ARP), a benchmark problem in global trajectory optimization. Experimental results demonstrate the framework's scalability and ability to generate robust heuristic solutions for ARP instances. Many of these solutions are exact, contingent on the assumed quality of the inner problem's optimizer.
2023-12-03
ArXiv (prépublication)
arxiv.org
arxiv.org
An Exact Framework for Solving the Space-Time Dependent TSP
Isaac Rudich
Quentin Cappart
Manuel L'opez-Ib'anez
Michael Romer
Louis-Martin Rousseau
2023-12-03
ArXiv (prépublication)
arxiv.org
arxiv.org
Can We Learn Communication-Efficient Optimizers?
Charles-Etienne Joseph
Benjamin Therien
Abhinav Moudgil
Boris Knyazev
Eugene Belilovsky
2023-12-02
ArXiv (prépublication)
doi.org
arxiv.org
Advancing Clinical Psychiatry: Integration of Clinical and Omics Data Using Machine Learning
Bill Qi
Yannis Trakadis
2023-12-01
Biological Psychiatry (publié)
doi.org
Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge
Vincent Andrearczyk
Valentin Oreiller
Sarah Boughdad
Catherine Cheze Le Rest
Olena Tankyevych
Hesham M. Elhalawani
Mario Jreige
John O. Prior
Martin Vallières
Dimitris Visvikis
Mathieu Hatt
Adrien Depeursinge
2023-12-01
Medical Image Analysis (publié)
doi.org
Balaur: Language Model Pretraining with Lexical Semantic Relations
Andrei Mircea
Jackie Cheung
2023-12-01
Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)
doi.org
openreview.net
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Avi Singh
John D Co-Reyes
Rishabh Agarwal
Ankesh Anand
Piyush Patil
Xavier Garcia
Peter J. Liu
James Harrison
Jaehoon Lee
Kelvin Xu
Aaron T Parisi
Abhishek Kumar
A. Alemi
Alex Rizkowsky
Azade Nova
Ben Adlam
Bernd Bohnet
Hanie Sedghi
Gamaleldin Fathy Elsayed
Igor Mordatch … (voir 21 de plus)
Isabelle Simpson
Izzeddin Gur
Jasper Snoek
Jeffrey Pennington
Jiri Hron
Kathleen Kenealy
Kevin Swersky
Kshiteej Mahajan
Laura Culp
Lechao Xiao
Maxwell Bileschi
Noah Constant
Roman Novak
Rosanne Liu
Tris Brian Warkentin
Yundi Qian
Ethan Dyer
Behnam Neyshabur
Jascha Sohl-Dickstein
Yamini Bansal
Noah Fiedel
Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often lim… (voir plus)ited by the quantity and diversity of high-quality human data. In this paper, we explore whether we can go beyond human data on tasks where we have access to scalar feedback, for example, on math problems where one can verify correctness. To do so, we investigate a simple self-training method based on expectation-maximization, which we call ReST
2023-12-01
arXiv (publié)
doi.org
arxiv.org
Brain decoding of the Human Connectome Project tasks in a dense individual fMRI dataset
Shima Rastegarnia
Marie St-Laurent
Elizabeth DuPre
Basile Pinsard
Lune Bellec
2023-12-01
NeuroImage (publié)
doi.org
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
Parishad BehnamGhader
Santiago Miret
Siva Reddy
Augmenting pretrained language models with retrievers to select the supporting documents has shown promise in effectively solving common NLP… (voir plus) problems, including language modeling and question answering, in an interpretable way. In this paper, we first study the strengths and weaknesses of different retriever-augmented language models (REALM,
2023-12-01
Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)
doi.org
openreview.net
Cross-lingual Open-Retrieval Question Answering for African Languages
Odunayo Ogundepo
Tajuddeen Gwadabe
Clara E. Rivera
Jonathan H. Clark
Sebastian Ruder
David Ifeoluwa Adelani
Bonaventure F. P. Dossou
Abdou Aziz DIOP
Claytone Sikasote
Gilles Q. Hacheme
Happy Buzaaba
Ignatius Majesty Ezeani
Rooweither Mabuya
Salomey Osei
Chris Emezue
Albert Njoroge Kahira
Shamsuddeen Hassan Muhammad
Akintunde Oladipo
Abraham Toluwase Owodunni
Atnafu Lambebo Tonja … (voir 24 de plus)
Iyanuoluwa Shode
Akari Asai
Aremu Anuoluwapo
Ayodele Awokoya
Bernard Opoku
Chiamaka Ijeoma Chukwuneke
Christine Mwase
Clemencia Siro
Stephen Arthur
Tunde Oluwaseyi Ajayi
V. Otiende
Andre Niyongabo Rubungo
B. Sinkala
Daniel A. Ajisafe
Emeka Onwuegbuzia
Falalu Lawan
Ibrahim Ahmad
Jesujoba Alabi
CHINEDU EMMANUEL MBONU
Mofetoluwa Adeyemi
Mofya Phiri
Orevaoghene Ahia
Ruqayya Nasir Iro
Sonia Adhiambo
2023-12-01
Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)
doi.org
Cross-lingual Open-Retrieval Question Answering for African Languages
Odunayo Ogundepo
Tajuddeen Gwadabe
Clara E. Rivera
Jonathan H. Clark
Sebastian Ruder
David Ifeoluwa Adelani
Bonaventure F. P. Dossou
Abdou Aziz DIOP
Claytone Sikasote
Gilles HACHEME
Happy Buzaaba
Ignatius Ezeani
Rooweither Mabuya
Salomey Osei
Chris Emezue
Albert Kahira
Shamsuddeen Hassan Muhammad
Akintunde Oladipo
Abraham Toluwase Owodunni
Atnafu Lambebo Tonja … (voir 32 de plus)
Iyanuoluwa Shode
Akari Asai
Aremu Anuoluwapo
Ayodele Awokoya
Bernard Opoku
Chiamaka Ijeoma Chukwuneke
Christine Mwase
Clemencia Siro
Stephen Arthur
Oyinkansola Awosan
Tunde Oluwaseyi Ajayi
Verrah Akinyi Otiende
Andre Niyongabo Rubungo
Boyd Sinkala
Daniel Ajisafe
Emeka Felix Onwuegbuzia
Falalu Lawan
Ibrahim Ahmad
Jesujoba Oluwadara Alabi
Habib Mbow
CHINEDU EMMANUEL MBONU
Emile Niyomutabazi
Mofetoluwa Adeyemi
Eunice Mukonde
Mofya Phiri
Orevaoghene Ahia
Ruqayya Nasir Iro
Sonia Adhiambo
Martin Namukombo
Neo Putini
Ndumiso Mngoma
Priscilla A. Amuok
2023-12-01
Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)
doi.org
openreview.net
Current AI applications in neurology: Brain imaging
Tal Arbel
Joshua D. Durso-Finley
Jean-Pierre R. Falet
Raghav Mehta
Douglas Arnold
Nick Pawlowski
2023-12-01
Journal of the Neurological Sciences (publié)
doi.org